Python MulticlassLibSVM.apply примеры использования

Пример #1

Файл: classifier_multiclasslibsvm_modular.py Проект: 42MachineLearning/shogun

def classifier_multiclasslibsvm_modular (fm_train_real=traindat,fm_test_real=testdat,label_train_multiclass=label_traindat,width=2.1,C=1,epsilon=1e-5):
	from modshogun import RealFeatures, MulticlassLabels
	from modshogun import GaussianKernel
	from modshogun import MulticlassLibSVM

	feats_train=RealFeatures(fm_train_real)
	feats_test=RealFeatures(fm_test_real)
	kernel=GaussianKernel(feats_train, feats_train, width)

	labels=MulticlassLabels(label_train_multiclass)

	svm=MulticlassLibSVM(C, kernel, labels)
	svm.set_epsilon(epsilon)
	svm.train()

	kernel.init(feats_train, feats_test)
	out = svm.apply().get_labels()
	predictions = svm.apply()
	return predictions, svm, predictions.get_labels()

Пример #2

Файл: classifier_multiclasslibsvm_modular.py Проект: manantomar/test

def classifier_multiclasslibsvm_modular(fm_train_real=traindat,
                                        fm_test_real=testdat,
                                        label_train_multiclass=label_traindat,
                                        width=2.1,
                                        C=1,
                                        epsilon=1e-5):
    from modshogun import RealFeatures, MulticlassLabels
    from modshogun import GaussianKernel
    from modshogun import MulticlassLibSVM

    feats_train = RealFeatures(fm_train_real)
    feats_test = RealFeatures(fm_test_real)
    kernel = GaussianKernel(feats_train, feats_train, width)

    labels = MulticlassLabels(label_train_multiclass)

    svm = MulticlassLibSVM(C, kernel, labels)
    svm.set_epsilon(epsilon)
    svm.train()

    kernel.init(feats_train, feats_test)
    out = svm.apply().get_labels()
    predictions = svm.apply()
    return predictions, svm, predictions.get_labels()

Пример #3

Файл: predict_multiclass_svm.py Проект: 42MachineLearning/shogun

def main(classifier, testset, output):
	LOGGER.info("SVM Multiclass evaluation")

	svm = MulticlassLibSVM()
	serialized_classifier = SerializableHdf5File(classifier, 'r')
	with closing(serialized_classifier):
		svm.load_serializable(serialized_classifier)

	test_feats, test_labels = get_features_and_labels(LibSVMFile(testset))
	predicted_labels = svm.apply(test_feats)

	with open(output, 'w') as f:
		for cls in predicted_labels.get_labels():
			f.write("%s\n" % int(cls))

	LOGGER.info("Predicted labels saved in: '%s'" % output)

Пример #4

Файл: predict_multiclass_svm.py Проект: manantomar/test

def main(classifier, testset, output):
    LOGGER.info("SVM Multiclass evaluation")

    svm = MulticlassLibSVM()
    serialized_classifier = SerializableHdf5File(classifier, 'r')
    with closing(serialized_classifier):
        svm.load_serializable(serialized_classifier)

    test_feats, test_labels = get_features_and_labels(LibSVMFile(testset))
    predicted_labels = svm.apply(test_feats)

    with open(output, 'w') as f:
        for cls in predicted_labels.get_labels():
            f.write("%s\n" % int(cls))

    LOGGER.info("Predicted labels saved in: '%s'" % output)